Software development for quantitative analysis of brain amyloid PET

Abstract Introduction Centiloid (CL) scaling has become a standard quantitative measure in amyloid PET because it allows the direct comparison of results across sites, even when different analytical methods or PET tracers are used. Methods In the present study, we developed new standalone software to easily handle a pipeline for accurate calculation of the CL scale for the five currently available amyloid PET tracers—11C‐PiB, 18F‐florbetapir, 18F‐flutemetamol, 18F‐florbetaben, and 18F‐NAV4694. This pipeline requires reorientation and coregistration of PET and MRI, anatomic standardization of coregistered PET to a standardized space using a warping parameter for coregistered MRI, application of standard volumes of interest (VOIs) to the warped PET, calculation of the standardized uptake value ratio (SUVR) for the target VOIs, and finally conversion of the SUVR to the CL scale. The PET data for these tracers were collected from the publicly available Global Alzheimer's Association Interactive Network (GAAIN) repository. We also developed software to map Z‐scores for the statistical comparison of a patient's PET data with a negative control database obtained from young healthy controls in the GAAIN repository. Results When whole cerebellum or whole cerebellum plus brainstem was chosen as the reference area, an excellent correlation was found between the CL scale calculated by this software and the CL scale published by GAAIN. There were no significant differences in the detection performance of significant amyloid accumulation using Z‐score mapping between each 18F‐labeled tracer and 11C‐PiB. The cutoff CL values providing the most accurate detection of regional amyloid positivity in Z‐score mapping were 11.8, 14.4, 14.7, 15.6, and 17.7 in the posterior cingulate gyrus and precuneus, frontal cortex, temporal cortex, parietal cortex, and striatum, respectively. Conclusion This software is able to not only provide reliable calculation of the global CL scale but also detect significant local amyloid accumulation in an individual patient.


INTRODUCTION
Amyloid positron emission tomography (PET) increases the diagnostic accuracy of Alzheimer's disease (AD) and non-AD. Because the binary classification of positive and negative amyloid PET findings is routinely based on visual interpretation, equivocation is inevitable and leads to interrater variability (Hosokawa et al., 2015). Equivocal findings should be avoided when the indication is being determined for the disease-modifying drugs currently under development. Accordingly, quantitative analysis has been proposed as an aid to visual interpretation (Collij et al., 2019;Matsuda et al., 2021).
The standardized uptake value ratio (SUVR) has been widely applied to the quantitative analysis of amyloid PET. However, SUVR values depend not only on the target and reference regions used, but also on the particular amyloid PET tracer. This variability can be resolved through a Centiloid (CL) scaling process that standardizes the quantitative amyloid imaging measures by standardizing the outcome of each analytical method or PET ligand to a scale from 0 to 100 (Klunk et al., 2015). The CL scale offers a direct comparison of results across institutions, even when different analytical methods or tracers are used, and may enable the clear definition of cutoffs for amyloid positivity. To determine the CL scale, it is necessary to follow the method put forward by the Global Alzheimer's Association Interactive Network (GAAIN, http://www.gaain.org/centiloidproject). However, this approach is time-consuming because it requires numerous steps to process the PET images and the corresponding MRI data and the use of multiple software packages. Furthermore, to more reliably determine amyloid positivity, the local pattern of amyloid accumulation should be captured, in addition to the global CL scale. To resolve these issues, we have developed standalone software for both calculating the global CL scale and detecting which brain regions have statistically significant amyloid accumulation. In the present study, we describe the details of this new software and its validation.

Data availability
This study was conducted using datasets collected from the publicly available GAAIN repository. These datasets comprised 495 PET images obtained using five different amyloid PET tracers ( 11 C-PiB, 18 Fflorbetapir, 18 F-flutemetamol, 18 F-florbetaben, and 18 F-NAV4694) and the corresponding three-dimensional T1-weighted MRI data of patients with AD, frontotemporal dementia, and mild cognitive impairment and of young and elderly healthy controls (Table 1). Acquisition of PET scan images were done from 50 to 70 min for 11 C-PiB and 18 F-NAV4694, from 50 to 60 min for 18 F-florbetapir, and 90 to 110 min for 18 F-flutemetamol and 18 F-florbetaben after administration of the PET tracer.

Processing pipeline of the software
The software developed in this study comprises two distinct processes: calculation of the CL scale from each subject's amyloid PET and MRI, and a statistical comparison of each subject's amyloid PET with a database of negative amyloid PET results obtained from young healthy controls.
The first process for quantitative analysis using the SUVR and a 100point scale termed the CL scale is illustrated in Figure 1 F I G U R E 1 Processing pipeline for quantitative measurements of amyloid accumulation in the target area of the cerebral cortex and striatum. The subject MRI was reoriented by setting the origin around the anterior commissure and coregistered to the Montreal Neurological Institute (MNI) template (avg152T1.nii). The subject PET was then reoriented also by setting the origin around the anterior commissure and coregistered to the coregistered subject MRI. Then, the coregistered subject MRI was warped into MNI space using unified segmentation. The parameters of the deformation field in this warping are applied to the coregistered subject PET images for anatomic standardization into MNI space. These translations were performed using the Statistical Parametric Mapping (SPM) 12 software. The SUVR is calculated from the amyloid PET counts in the cerebral cortical and striatal (ctx) volumes of interest (VOIs) and in a reference VOI using Global Alzheimer's Association Initiative Network (GAAIN) standard VOI templates. Next, the SUVR is converted to CL values using a direct conversion equation. Processing with a black background was SPM12 for anatomic standardization into MNI space. These translations were to CL values using direct conversion equations (Table 2) for each PET tracer, as described in previous reports (Battle et al., 2018;Klunk et al., 2015;Navitsky et al., 2018;Rowe et al., 2016;Rowe et al., 2017).
The second process, which involves comparison of each subject's PET data with a negative normal database comprising young healthy controls, is illustrated in Figure 3. First, the standardized subject PET images are smoothed using an 8-mm 3 Gaussian kernel. for 18 F-florbetaben, and 10 for 18 F-NAV4694). A CL score less than 10 has been reported to be optimal for excluding neuritic plaques in comparisons of amyloid PET measures with neuropathological findings (Amadoru et al., 2020). A Z-score map for masked, smoothed, and standardized subject PET data is displayed by overlay on tomographic sections with a contour of the target cortical and striatal areas and with surface rendering (Figure 4) of the standardized brain MRI data using the following equation: Z-score = ([individual count] -[mean count of control database])/(standard deviation count of control database). In

F I G U R E 3
Processing pipeline for comparison of each subject's PET data with a negative normal database comprising young healthy controls. The standardized subject PET is smoothed using an 8-mm 3 Gaussian kernel. The smoothed and standardized subject PET images are then masked to remove white matter areas with high counts after normalization of the PET count using the reference VOI count. A Z-score is calculated from a comparison of masked, smoothed, and standardized subject PET images with masked mean and standard deviation PET images generated from an amyloid-negative control database comprising smoothed and standardized subject PET images of young healthy controls from the GAAIN dataset repository. A Z-score map is displayed by overlay on tomographic sections with a contour of the target cortical and striatal area and surface rendering of the standardized brain MRI the Z-score mapping display, we can change the upper and lower Zscore levels and the cluster size threshold.
After reorientation of the PET and MRI images using SPM12, these two processes run automatically and sequentially in standalone software on a Windows operating system. The software, named "Amyquant," requires about 5 min to complete all of the steps for a single subject using a 64-bit laptop (CPU, Intel ® Core™ i7, 1.90 GHz; memory, 16 GB).

Validation of the software for CL calculation
The CL scales calculated using the present software were compared with the CL scales published on the GAAIN website for each PET tracer and each reference VOI. For validation, as defined by Klunk et al. (2015), the slope should be between 0.98 and 1.02 and the intercept between −2 and +2 CL for a linear regression equation and the R 2 correlation coefficient should exceed 0.98.
F I G U R E 4 Z-score mapping on tomographic sections and surface rendering of the standardized brain MRI. The target cortical and striatal areas are contoured by white lines. The lower and upper levels of the Z-score can be changed along with a threshold of a cluster size

Evaluation of Z-score mapping
To evaluate the regional detectability of significant amyloid accumulation using each 18 F-labeled tracer, we compared the presence or absence of areas with significant amyloid accumulation in five regions (the posterior cingulate cortex and precuneus, frontal cortex, temporal cortex, parietal cortex, and striatum; Figure 2c) of the target cortical areas between each 18 F-labeled tracer and the corresponding 11 C-PiB PET images from the same individuals (χ 2 test). To avoid false positives, we set a Z-score threshold of 2.6 corresponding to p < 0.01 with sufficiently large cluster size of 300 voxels (2.4 cc). We then performed receiver operating characteristic (ROC) analysis on the pooled data of all PET tracers to evaluate the relationship between the global CL scale and the positive or negative findings for regional amyloid accumulation.

RESULTS
When the whole cerebellum or whole cerebellum plus brainstem was chosen as the reference VOI, an excellent correlation between the CL scales calculated by this software and those published on the GAAIN website was confirmed by the fact that the correlation coefficient, as well as the slope and intercept of the linear regression equation, were within the range allowed by Klunk et al. (2015) in each of the four chosen reference regions using the five different PET tracers (Table 3).
When cerebellar gray matter was chosen as the reference VOI, the intercept for 18 F-florbetaben exceeded the allowed range. When the pons was chosen as the reference VOI, the slope for 18 F-florbetaben and 18 F-NAV4694 and the correlation coefficient for 18 F-florbetaben exceeded the allowed ranges.
A comparison of the detection performance of significant amyloid accumulation in the five regions using Z-score mapping revealed no statistically significant difference (p > 0.3) between each 18 F-labeled tracer and the corresponding 11 C-PiB in the same individuals (Table 4).
In the 495 amyloid PET studies, ROC analysis between Z-score mapping and the global CL scale showed that the CL scale could determine the positivity of local amyloid accumulation with high accuracy (

DISCUSSION
In the present study, we developed standalone software for quantify-

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This study has some limitations. First, our newly developed software is not fully automatic. The first step requires setting of the origin of the PET and MRI images around the anterior commissure to avoid inaccurate coregistration due to the large distance between the PET and MRI origins. Semiautomatic or automatic reorientation would be preferable. Second, the small number of young control subjects from the amyloid-negative GAAIN database comprising datasets for 18 F-labeled tracers could cause false-positive or -negative findings in Z-score analysis. Although there were no significant differences in the detection performance of regional positivity between 18 F-labeled tracers and 11 C-PiB, a larger negative database may be necessary to increase the accuracy of Z-score analysis for 18 F-labeled tracers. Third, a longitudinal study in the same individuals may be necessary for more accurate comprehension of the spatial and temporal ordering of amyloid pathology in AD.

CONCLUSIONS
We developed standalone quantitative software for amyloid PET. In addition to reliably calculating the global CL scale, this software can detect significant local amyloid accumulation in an individual patient by comparison with a negative database of young healthy controls. This software can be applied to the five currently available amyloid PET tracers.

ACKNOWLEDGMENTS
This study was based on the GAAIN datasets. We are grateful to the project participants and the researchers who collected these datasets and made them freely accessible. We would also like to thank Dai Nip-